Search Results (5)

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

Generalized linear models (GLMs) are popular tools for simulating daily rainfall series. However, the application of GLMs in drought-prone areas is challenging, as there is inconsistency in rainfall data during long and irregular periods. The majority of studies include regions where rainfall is well distributed during the year indicating the capabilities of the GLM approach. In many cases, the summer period has been discarded from the analyses, as it affects predictive performance of the model. In this paper, a two-stage (occurrence and amounts) GLM is used to simulate daily rainfall in two Greek islands. Summer (June–August) smooth adjustments have been proposed to model the low probability of rainfall during summer, and consequently, to improve the simulations during autumn. Preliminary results suggest that the fitted models simulate adequate rainfall occurrence and amounts in Milos and Naxos islands, and can be used as input in future hydrological applications. Full article

Crop insurance is a viable solution to reduce the vulnerability of smallholder farmers to risks from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security. In developing and emerging countries, the implementation of area yield-based insurance, the form of crop insurance preferred by clients and industry, is constrained by the limited availability of detailed historical yield records. Remote-sensing technology can help to fill this gap by providing an unbiased and replicable source of the needed data. This study is dedicated to demonstrating and validating the methodology of remote sensing and crop growth model-based rice yield estimation with the intention of historical yield data generation for application in crop insurance. The developed system combines MODIS and SAR-based remote-sensing data to generate spatially explicit inputs for rice using a crop growth model. MODIS reflectance data were used to generate multitemporal LAI maps using the inverted Radiative Transfer Model (RTM). SAR data were used to generate rice area maps using MAPScape-RICE to mask LAI map products for further processing, including smoothing with logistic function and running yield simulation using the ORYZA crop growth model facilitated by the Rice Yield Estimation System (Rice-YES). Results from this study indicate that the approach of assimilating MODIS and SAR data into a crop growth model can generate well-adjusted yield estimates that adequately describe spatial yield distribution in the study area while reliably replicating official yield data with root mean square error, RMSE, of 0.30 and 0.46 t ha⁻¹ (normalized root mean square error, NRMSE of 5% and 8%) for the 2016 spring and summer seasons, respectively, in the Red River Delta of Vietnam, as evaluated at district level aggregation. The information from remote-sensing technology was also useful for identifying geographic locations with peculiarity in the timing of rice establishment, leaf growth, and yield level, and thus contributing to the spatial targeting of further investigation and interventions needed to reduce yield gaps. Full article

Satellite-based PM_2.5 concentration estimation is growing as a popular solution to map the PM_2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM_2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM_2.5 concentration mapping. In this process, the linear and non-linear relationships between PM_2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R² and lower RMSEs. This is confirmed by the cross-validation-based adjusted R² with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the “straight line” relations, the “smoothed curves” from GAM-based apportionment analysis reveals that factors contributing to PM_2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM_2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM_2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM_2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM_2.5. Full article

The short-term effects of high temperatures are a serious concern in the context of climate change. In areas that today have mild climates the research activity has been rather limited, despite the fact that differences in temperature susceptibility will play a fundamental role in understanding the exposure, acclimatization, adaptation and health risks of a changing climate. In addition, many studies employ biometeorological indexes without careful investigation of the regional heterogeneity in the impact of relative humidity. We aimed to investigate the effects of summer temperature and relative humidity and regional differences in three regions of Sweden allowing for heterogeneity of the effect over the scale of summer temperature. To do so, we collected mortality data for ages 65+ from Stockholm, Göteborg and Skåne from the Swedish National Board of Health and Welfare and the Swedish Meteorological and Hydrological Institute for the years 1998 through 2005. In Stockholm and Skåne on average 22 deaths per day occurred, while in Göteborg the mean frequency of daily deaths was 10. We fitted time-series regression models to estimate relative risks of high ambient temperatures on daily mortality using smooth functions to control for confounders, and estimated non-linear effects of exposure while allowing for auto-regressive correlation of observations within summers. The effect of temperature on mortality was found distributed over the same or following day, with statistically significant cumulative combined relative risk of about 5.1% (CI = 0.3, 10.1) per °C above the 90th percentile of summer temperature. The effect of high relative humidity was statistically significant in only one of the regions, as was the effect of relative humidity (above 80th percentile) and temperature (above 90th percentile). In the southernmost region studied there appeared to be a significant increase in mortality with decreasing low summer temperatures that was not apparent in the two more northerly situated regions. The effects of warm temperatures on the elderly population in Sweden are rather strong and consistent across different regions after adjustment for mortality displacement. The impact of relative humidity appears to be different in regions, and may be a more important predictor of mortality in some areas. Full article